The parameters also include feature subset selections. [1]} eggert2015classification used the WEKA framework to evaluate three feature selectors. The results indicated that WrapperSubsetEval, a wrapper with best-first search, produced the highest accuracy. We also analyzed this “wrapper”[2]} subset selector which uses the algorithm itself to select the best combination of features. We compared its results to CfsSubsetEval [3]}, a correlation-based “filter” [2]} method which greedily selects the optimal feature subset independently of the algorithm. It favors features that are highly correlated to the class but uncorrelated to each other to minimize feature redundancy. CfsSubsetEval is based on information theory and we have demonstrated high accuracy on various datasets in the past [5]}.
